We investigate a new registration method for ultrasound volumes relying on on a statistical texture-based similarity measure. Texture information is given by spatial Gabor filters and represented by statistical kernel-based distributions. The registration similarity measure is then defined as a probabilistic distance, derived from Bhattacharyya coefficient, between two statistical distributions. Given this similarity measure, parametric ultrasound image registration is stated as a robust minimization issue. We also exploit frequency properties of spatial Gabor filters to propose a multiresolution approach to perform this minimization. We provide a preliminary evaluation of the new registration technique on clinical data.

Abstract. 3D registration of ultrasound images is an important and fast-growing research area with various medical applications, such as image-guided radiotherapy and surgery. However, this registration process is extremely challenging due to the deformation of soft tissue and the existence of speckles in these images. This paper presents a novel intra-modality elastic registration technique for 3D ultrasound images. It uses the general concept of attribute vectors to find the corresponding voxels in the fixed and moving images. The method does not require any pre-segmentation and does not employ any numerical optimization procedure. Therefore, the computational requirements are very low and it has the potential to be used for real-time applications. The technique is implemented and tested for 3D ultrasound images of liver, captured by a 3D ultrasound transducer. The results show that the method is sufficiently accurate and robust and is not easily trapped with local minima. 1

Abstract. We present an intensity based deformable registration algorithm for 3D ultrasound data. The proposed method uses a variational approach and combines the characteristics of a multilevel algorithm and the properties of ultrasound data in order to provide a fast and accurate deformable registration method. In contrast to previously proposed approaches, we use no feature points and no interpolation technique, but compute a dense displacement field directly. We demonstrate that this approach, although it includes solving large PDE systems, reduces the computation time if implemented using efficient numerical techniques. The performance of the algorithm is tested on multiple 3D US images of the liver. Validation is performed by simulations, similarity comparisons between original and deformed images, visual inspection of the displacement fields and visual assessment of the deformed images by physicians. 1

This thesis presents research that was conducted to develop anatomically realis-tic finite element models of breast deformation under a variety of gravity loading conditions to assist clinicians in tracking suspicious tissues across multiple imaging modalities. Firstly, the accuracy of the modelling framework in predicting deformations of a homogeneous body was measured using custom designed silicon gel phantoms. The model predicted surface deformations with an average RMS error of 1.5 mm ± 0.2 mm and tracked internal marker locations with an average RMS error of 1.4 mm ± 0.7 mm. A novel method was then developed to determine the reference configuration of a body, when given its mechanical properties, boundary conditions and a deformed configuration. The theoretical validity of the technique was confirmed with an an-alytic solution. The accuracy of the method was also measured using silicon gel experiments, predicting the reference configuration surface with an average RMS

for brain sinking measurement

This paper considers registration of 3D ultrasound volumes. One way to acquire 3D data is to use a mechanically-swept 3D probe. However, the usefulness of these probes is restricted by their limited field of view. While this problem can be overcome by attaching a position sensor to the probe, an external position sensor can be an inconvenience in a clinical setting and does not align the volumes correctly when there is tissue displacement or deformation. The objective of this paper is to replace the 6 degree of freedom (DOF) sensor with a combination of 3 DOF image registration and an integrated intertial sensor for measuring orientation. We examine a range of optimisation algorithms and similarity measures for registration and compare them in in vitro and in vivo experiments. We register based on multiple reslice images rather than a whole voxel array. In this paper, we use a large number of reslices for improved reliability at the expense of computational speed. We have found that the Levenberg-Marquardt method is very fast but is not guaranteed to give the correct solution all the time. We conclude that normalised mutual information used in the Nelder-Mead simplex algorithm is potentially suitable for the registration task with an average execution time of around five minutes, in the majority of cases, with two restarts in a C++ implementation on a 3.0 GHz Intel Core 2 Duo CPU machine. 1